where, \(X^{(\mathrm {K})}\)  is the node feature matrix of the \(\mathrm {K}^{th}\)  layer, \(W^{^{\prime }}\)  and \(b^{^{\prime }}\)  are the learnable parameters of the linear layer, and \(\hat{y_{i}}\)  is the state estimate obtained using the GNN for the \(i^{th}\)  train/test sample.
More details about the learning process of the GNNs can be found in [1]}.
